SSN: A Stair-Shape Network for Real-Time Polyp Segmentation in Colonoscopy Images

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SSN: A Stair-Shape Network for Real-Time Polyp Segmentation in Colonoscopy Images


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SSN: A Stair-Shape Network for Real-Time Polyp Segmentation in Colonoscopy Images

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Colorectal cancer is one of the most life-threatening malignancies, commonly occurring from intestinal polyps. Currently, clinical colonoscopy exam is an effective way for early detection of polyps and is often conducted in real-time manner. But, colonoscopy analysis is time-consuming and suffers a high miss rate. In this paper, we develop a novel stair-shape network (SSN) for real-time polyp segmentation in colonoscopy images (not merely for simple detection). Our new model is much faster than U-Net, yet yields better performance for polyp segmentation. The model first utilizes four blocks to extract spatial features at the encoder stage. The subsequent skip connection with a Dual Attention Module for each block and a final Multi-scale Fusion Module are used to fully fuse features of different scales. Based on abundant data augmentation and strong supervision of auxiliary losses, our model can learn much more information for polyp segmentation. Our new polyp segmentation method attains high performance on several datasets (CVC-ColonDB, CVC-ClinicDB, and EndoScene), outperforming state-of-the-art methods. Our network can also be applied to other imaging tasks for real-time segmentation and clinical practice.
Colorectal cancer is one of the most life-threatening malignancies, commonly occurring from intestinal polyps. Currently, clinical colonoscopy exam is an effective way for early detection of polyps and is often conducted in real-time manner. But, colonoscopy analysis is time-consuming and suffers a high miss rate. In this paper, we develop a novel stair-shape network (SSN) for real-time polyp segmentation in colonoscopy images (not merely for simple detection). Our new model is much faster than U-Net, yet yields better performance for polyp segmentation. The model first utilizes four blocks to extract spatial features at the encoder stage. The subsequent skip connection with a Dual Attention Module for each block and a final Multi-scale Fusion Module are used to fully fuse features of different scales. Based on abundant data augmentation and strong supervision of auxiliary losses, our model can learn much more information for polyp segmentation. Our new polyp segmentation method attains high performance on several datasets (CVC-ColonDB, CVC-ClinicDB, and EndoScene), outperforming state-of-the-art methods. Our network can also be applied to other imaging tasks for real-time segmentation and clinical practice.